On the other hand, we design a communication reward \(r_{c}\)  and apply DDPG [1]} to learn the leader-follower forest.
First, inspired by VBC [2]}, we use the variance between the largest and the second-largest action values to model the confidence
of agent \(i\) 's decision. Therefore, an intrinsic reward for the improvement of decision confidence is formulated as,
\(r_{int}^t=\sum _{i\in N}{[v_i(h_i^t,aggr_{i}^t)-v_i(h_i^t,0)]}\) 
